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6/23/2020
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Deep Learning on ARM Processors - From Ground Up is the name of an in-depth learning course using ARM processors. In this course we will go on an interesting journey together in which we will learn how to build deep neural networks from the basis of working on microcontrollers. This course will start by learning the basics of deep learning with practical code that will show all the basic building blocks that lead to building a huge neural network. All this, whether training or inference, is done on our microcontrollers. This course teaches the useful use of popular libraries such as Keras and Tensorflow along with popular deep learning libraries for microcontrollers such as CMSIS-NN, CubeMX.AI and TensorFlow Lite.
Build neural networks from the ground up without the use of bookstores
Master quantization methods for extending neural networks on microcontrollers
Build a Human Activity Recognition (HAR) firmware such as walking, running and
Build a deep learning firmware for handwriting recognition
Build an in-depth learning firmware for Acoustic Scene Classification (ASC)
You will be able to give a lecture on deep learning.
Publisher: Udemy Instructor: Israel Gbati and EmbeddedExpertIO English language Level of training: basic to advanced Number of courses: 118 Duration: 19 hours and 16 minutes
1. Introduction 2. Building Blocks of Neural Networks 3. Introduction to Neural Network (Part 2) 4. Logistic Regression 5. Deep Neural Networks 6. Improving Neural Networks with Regularization Techniques 7. Building A Logistic Regression Model 8. Building Deep Neural Networks From Scratch 9. Convolutional Neural Networks (CNN) 10. CubeMX 5 & CubeIDE Primer 11. CubeMX AI 12. Case Study Deploying the MNIST Handwriting Recognition Model on ARM MCUs 13. Set Up 14. Python Essentials 15. CubeMX Primer 16. Closing
STM32F411-NUCLEO BOARD STM32F429 -DISCO BOARD
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